Towards Efficient Query Processing on Massive Time-Evolving Graphs

Fard, Arash; Abdolrashidi, Amir; Ramaswamy, Lakshmish; Miller, John A.

doi:10.4108/icst.collaboratecom.2012.250532

Cited by 23 publications

(14 citation statements)

References 30 publications

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“…Moreover, real-world data graphs are evolving over time; i.e., there are minor changes in their structure through the time. Hence, it should be possible to design incremental algorithms for pattern problems in many applications (Fard, Abdolrashidi, Ramaswamy, & Miller, 2012).…”

Section: Edge-labeled Graphsmentioning

confidence: 99%

Directions for Big Data Graph Analytics Research

Miller¹,

Ramaswamy²,

Kochut³

et al. 2015

STBD

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In the era of big data, interest in analysis and extraction of information from massive data graphs is increasing rapidly. This paper examines the field of graph analytics from a query processing point of view. Whether it be determination of shortest paths or finding patterns in a data graph matching a query graph, the issue is to find interesting characteristics or information content from graphs. Many of the associated problems can be abstracted to problems on paths or problems on patterns. Unfortunately, seemingly simple problems, such as finding patterns in a data graph matching a query graph are surprisingly difficult (e.g., dual simulation has cubic complexity and subgraph isomorphism is -hard). In addition, the iterative nature of algorithms in this field makes the simple MapReduce style of parallel and distributed processing less effective. Still, the need to provide answers even for very large graphs is driving the research. Progress, trends and directions for future research are presented.

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Section: Edge-labeled Graphsmentioning

confidence: 99%

Directions for Big Data Graph Analytics Research

Miller¹,

Ramaswamy²,

Kochut³

et al. 2015

STBD

Self Cite

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“…Existing works examine valuable insights into the dynamic world by posing queries on an evolving sequence of social graphs (e.g., Reference 32) and time-evolving graphs tend to be increasingly used as a paradigm also for the emerging area of OSNs [33]. However, the ability to process queries concerning the information diffusion in a scalable way remains to a great extent unstudied.…”

Section: Osn Evolutionmentioning

confidence: 99%

On the Impact of Online Social Networks in Content Delivery

Kilanioti

Georgiou

Pallis

2014

Advanced Content Delivery, Streaming, and Cloud Services

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“…These new models allow matches to be found in polynomial time. Moreover, some researchers (Brynielsson, Hogberg, Kaati, Martenson, & Svenson, 2010) (Fan, Li, Ma, Tang, Wu, & Wu, 2010) (Ma, Cao, Fan, Huai, & Wo, 2011) (Fard, Abdolrashidi, Ramaswamy, & Miller, 2012) believe that graph simulation can be more appropriate than subgraph isomorphism for some modern applications such as social network analysis because it yields matches that are conceptually more intuitive.…”

Section: Introductionmentioning

confidence: 99%

Distributed and Scalable Graph Pattern Matching: Models and Algorithms

Fard¹

2014

STBD

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Graph pattern matching is a fundamental operation for many applications, and it is exhaustively studied in its classical forms. Nevertheless, there are newly emerging applications, like analyzing hyperlinks of the web graph and analyzing associations in a social network, that need to process massive graphs in a timely manner. Regarding the extremely large size of these graphs and knowledge they represent, not only new computing platforms are needed, but also old models and algorithms should be revised. In recent years, a few pattern matching models have been introduced that can promise a new avenue for pattern matching research on extremely massive graphs. Moreover, several graph processing frameworks like Pregel have recently sought to harness shared nothing clusters for processing massive graphs through a vertex-centric, Bulk Synchronous Parallel (BSP) programming model. However, developing scalable and efficient BSP-based algorithms for pattern matching is very challenging on these platforms because this problem does not naturally align with a vertex-centric programming paradigm. This paper introduces a new pattern matching model, called tight simulation, which outperforms the previous models in its family in terms of scalability while preserving their important properties. It also presents a novel distributed algorithm based on the vertex-centric programming paradigm for this pattern matching model and several others in the family of graph simulation as well. Our algorithms are fine tuned to consider the challenges of pattern matching on massive data graphs. Furthermore, we present an extensive set of experiments involving massive graphs (millions of vertices and billions of edges) to study the effects of various parameters on the scalability and performance of the proposed algorithms.

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Towards Efficient Query Processing on Massive Time-Evolving Graphs

Cited by 23 publications

References 30 publications

Directions for Big Data Graph Analytics Research

Directions for Big Data Graph Analytics Research

On the Impact of Online Social Networks in Content Delivery

Distributed and Scalable Graph Pattern Matching: Models and Algorithms

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